Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations209
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.1 KiB
Average record size in memory608.2 B

Variable types

Text6
Categorical1
Numeric14

Alerts

Area (km²) is highly overall correlated with Population Density (per km²) and 1 other fieldsHigh correlation
Forest Area 2010 is highly overall correlated with Forest Area 2011 and 9 other fieldsHigh correlation
Forest Area 2011 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2012 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2013 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2014 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2015 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2016 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2017 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2018 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2019 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2020 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Population Density (per km²) is highly overall correlated with Area (km²)High correlation
Population Rank is highly overall correlated with Area (km²)High correlation
Country Name has unique values Unique
Capital has unique values Unique
Area (km²) has unique values Unique
Population Density (per km²) has unique values Unique
Population Rank has unique values Unique
Forest Area 2010 has unique values Unique
Forest Area 2011 has unique values Unique
Forest Area 2012 has unique values Unique
Forest Area 2013 has unique values Unique
Forest Area 2014 has unique values Unique
Forest Area 2015 has unique values Unique
Forest Area 2016 has unique values Unique
Forest Area 2017 has unique values Unique
Forest Area 2018 has unique values Unique
Forest Area 2019 has unique values Unique
Forest Area 2020 has unique values Unique
Country_normalized has unique values Unique
City_normalized has unique values Unique

Reproduction

Analysis started2025-04-06 20:57:35.461840
Analysis finished2025-04-06 20:57:48.456211
Duration12.99 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Country Name
Text

Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2025-04-06T23:57:48.580212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length22
Mean length9.5550239
Min length4

Characters and Unicode

Total characters1997
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)100.0%

Sample

1st rowAruba
2nd rowAfghanistan
3rd rowAngola
4th rowAlbania
5th rowAndorra
ValueCountFrequency (%)
and 8
 
2.7%
islands 7
 
2.3%
rep 7
 
2.3%
republic 6
 
2.0%
st 4
 
1.3%
new 3
 
1.0%
united 3
 
1.0%
arab 3
 
1.0%
guinea 3
 
1.0%
the 3
 
1.0%
Other values (244) 253
84.3%
2025-04-06T23:57:48.812212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 283
 
14.2%
i 159
 
8.0%
n 151
 
7.6%
e 142
 
7.1%
r 110
 
5.5%
o 95
 
4.8%
91
 
4.6%
u 72
 
3.6%
t 72
 
3.6%
s 70
 
3.5%
Other values (48) 752
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 283
 
14.2%
i 159
 
8.0%
n 151
 
7.6%
e 142
 
7.1%
r 110
 
5.5%
o 95
 
4.8%
91
 
4.6%
u 72
 
3.6%
t 72
 
3.6%
s 70
 
3.5%
Other values (48) 752
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 283
 
14.2%
i 159
 
8.0%
n 151
 
7.6%
e 142
 
7.1%
r 110
 
5.5%
o 95
 
4.8%
91
 
4.6%
u 72
 
3.6%
t 72
 
3.6%
s 70
 
3.5%
Other values (48) 752
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 283
 
14.2%
i 159
 
8.0%
n 151
 
7.6%
e 142
 
7.1%
r 110
 
5.5%
o 95
 
4.8%
91
 
4.6%
u 72
 
3.6%
t 72
 
3.6%
s 70
 
3.5%
Other values (48) 752
37.7%

Capital
Text

Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2025-04-06T23:57:48.978210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length14
Mean length7.784689
Min length4

Characters and Unicode

Total characters1627
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)100.0%

Sample

1st rowOranjestad
2nd rowKabul
3rd rowLuanda
4th rowTirana
5th rowAndorra la Vella
ValueCountFrequency (%)
san 4
 
1.7%
city 4
 
1.7%
town 3
 
1.2%
pago 2
 
0.8%
port 2
 
0.8%
saint 2
 
0.8%
oranjestad 1
 
0.4%
john’s 1
 
0.4%
andorra 1
 
0.4%
la 1
 
0.4%
Other values (220) 220
91.3%
2025-04-06T23:57:49.407052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 235
 
14.4%
o 119
 
7.3%
i 112
 
6.9%
n 104
 
6.4%
e 91
 
5.6%
r 91
 
5.6%
u 77
 
4.7%
s 67
 
4.1%
t 65
 
4.0%
l 61
 
3.7%
Other values (52) 605
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 235
 
14.4%
o 119
 
7.3%
i 112
 
6.9%
n 104
 
6.4%
e 91
 
5.6%
r 91
 
5.6%
u 77
 
4.7%
s 67
 
4.1%
t 65
 
4.0%
l 61
 
3.7%
Other values (52) 605
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 235
 
14.4%
o 119
 
7.3%
i 112
 
6.9%
n 104
 
6.4%
e 91
 
5.6%
r 91
 
5.6%
u 77
 
4.7%
s 67
 
4.1%
t 65
 
4.0%
l 61
 
3.7%
Other values (52) 605
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 235
 
14.4%
o 119
 
7.3%
i 112
 
6.9%
n 104
 
6.4%
e 91
 
5.6%
r 91
 
5.6%
u 77
 
4.7%
s 67
 
4.1%
t 65
 
4.0%
l 61
 
3.7%
Other values (52) 605
37.2%

Continent
Categorical

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Africa
54 
Asia
47 
Europe
45 
North America
34 
Oceania
17 

Length

Max length13
Median length7
Mean length7.1722488
Min length4

Characters and Unicode

Total characters1499
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth America
2nd rowAsia
3rd rowAfrica
4th rowEurope
5th rowEurope

Common Values

ValueCountFrequency (%)
Africa 54
25.8%
Asia 47
22.5%
Europe 45
21.5%
North America 34
16.3%
Oceania 17
 
8.1%
South America 12
 
5.7%

Length

2025-04-06T23:57:49.479053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-06T23:57:49.535671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
africa 54
21.2%
asia 47
18.4%
america 46
18.0%
europe 45
17.6%
north 34
13.3%
oceania 17
 
6.7%
south 12
 
4.7%

Most occurring characters

ValueCountFrequency (%)
a 181
12.1%
r 179
11.9%
i 164
10.9%
A 147
9.8%
c 117
 
7.8%
e 108
 
7.2%
o 91
 
6.1%
u 57
 
3.8%
f 54
 
3.6%
s 47
 
3.1%
Other values (10) 354
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 181
12.1%
r 179
11.9%
i 164
10.9%
A 147
9.8%
c 117
 
7.8%
e 108
 
7.2%
o 91
 
6.1%
u 57
 
3.8%
f 54
 
3.6%
s 47
 
3.1%
Other values (10) 354
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 181
12.1%
r 179
11.9%
i 164
10.9%
A 147
9.8%
c 117
 
7.8%
e 108
 
7.2%
o 91
 
6.1%
u 57
 
3.8%
f 54
 
3.6%
s 47
 
3.1%
Other values (10) 354
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 181
12.1%
r 179
11.9%
i 164
10.9%
A 147
9.8%
c 117
 
7.8%
e 108
 
7.2%
o 91
 
6.1%
u 57
 
3.8%
f 54
 
3.6%
s 47
 
3.1%
Other values (10) 354
23.6%

Area (km²)
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean649055.92
Minimum26
Maximum17098242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:49.626747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile262.2
Q113943
median109884
Q3488100
95-th percentile2273349.2
Maximum17098242
Range17098216
Interquartile range (IQR)474157

Descriptive statistics

Standard deviation1853060.8
Coefficient of variation (CV)2.8550094
Kurtosis38.824466
Mean649055.92
Median Absolute Deviation (MAD)109425
Skewness5.7546117
Sum1.3565269 × 108
Variance3.4338342 × 1012
MonotonicityNot monotonic
2025-04-06T23:57:49.722746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 1
 
0.5%
10452 1
 
0.5%
825615 1
 
0.5%
18575 1
 
0.5%
1267000 1
 
0.5%
923768 1
 
0.5%
130373 1
 
0.5%
41850 1
 
0.5%
323802 1
 
0.5%
147181 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
26 1
0.5%
34 1
0.5%
53 1
0.5%
54 1
0.5%
61 1
0.5%
151 1
0.5%
160 1
0.5%
180 1
0.5%
181 1
0.5%
199 1
0.5%
ValueCountFrequency (%)
17098242 1
0.5%
9984670 1
0.5%
9706961 1
0.5%
9372610 1
0.5%
8515767 1
0.5%
7692024 1
0.5%
3287590 1
0.5%
2780400 1
0.5%
2724900 1
0.5%
2381741 1
0.5%

Population Density (per km²)
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.2
Minimum0.0261
Maximum8416.4634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:49.820747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0261
5-th percentile5.60458
Q136.0935
median92.6221
Q3222.4774
95-th percentile596.44256
Maximum8416.4634
Range8416.4373
Interquartile range (IQR)186.3839

Descriptive statistics

Standard deviation633.18773
Coefficient of variation (CV)2.9018686
Kurtosis136.70201
Mean218.2
Median Absolute Deviation (MAD)67.338
Skewness10.818722
Sum45603.8
Variance400926.7
MonotonicityNot monotonic
2025-04-06T23:57:49.912749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
591.3611 1
 
0.5%
525.2334 1
 
0.5%
3.1092 1
 
0.5%
15.6097 1
 
0.5%
20.6851 1
 
0.5%
236.5759 1
 
0.5%
53.2962 1
 
0.5%
419.6897 1
 
0.5%
16.7828 1
 
0.5%
207.5511 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0.0261 1
0.5%
2.1727 1
0.5%
3.1092 1
0.5%
3.4032 1
0.5%
3.6204 1
0.5%
3.7621 1
0.5%
3.7727 1
0.5%
3.8513 1
0.5%
3.8717 1
0.5%
4.5194 1
0.5%
ValueCountFrequency (%)
8416.4634 1
0.5%
1924.4876 1
0.5%
1745.9567 1
0.5%
1687.6139 1
0.5%
1299.2647 1
0.5%
1188.5926 1
0.5%
1160.035 1
0.5%
844.063 1
0.5%
654.9651 1
0.5%
636.9946 1
0.5%

Population Rank
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.88038
Minimum1
Maximum227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:50.003747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.4
Q153
median107
Q3159
95-th percentile210.2
Maximum227
Range226
Interquartile range (IQR)106

Descriptive statistics

Standard deviation63.635758
Coefficient of variation (CV)0.5898733
Kurtosis-1.1436032
Mean107.88038
Median Absolute Deviation (MAD)53
Skewness0.078613028
Sum22547
Variance4049.5097
MonotonicityNot monotonic
2025-04-06T23:57:50.093063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198 1
 
0.5%
119 1
 
0.5%
145 1
 
0.5%
185 1
 
0.5%
54 1
 
0.5%
6 1
 
0.5%
106 1
 
0.5%
71 1
 
0.5%
120 1
 
0.5%
49 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
227 1
0.5%
222 1
0.5%
221 1
0.5%
220 1
0.5%
218 1
0.5%
216 1
0.5%
215 1
0.5%
214 1
0.5%
213 1
0.5%
212 1
0.5%

Forest Area 2010
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.644637
Minimum0
Maximum98.076026
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:50.181062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.44914528
Q111.072636
median30.693003
Q351.565097
95-th percentile73.408623
Maximum98.076026
Range98.076026
Interquartile range (IQR)40.492461

Descriptive statistics

Standard deviation24.478416
Coefficient of variation (CV)0.74984494
Kurtosis-0.47146605
Mean32.644637
Median Absolute Deviation (MAD)19.79634
Skewness0.5349734
Sum6822.7291
Variance599.19283
MonotonicityNot monotonic
2025-04-06T23:57:50.272063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.42619746 1
 
0.5%
8.926368594 1
 
0.5%
45.89824945 1
 
0.5%
0.950422357 1
 
0.5%
25.53880782 1
 
0.5%
34.80264251 1
 
0.5%
11.07263564 1
 
0.5%
33.13401452 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.009693053 1
0.5%
0.05730659 1
0.5%
0.065940027 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.241587575 1
0.5%
0.350729517 1
0.5%
0.356301543 1
0.5%
ValueCountFrequency (%)
98.07602564 1
0.5%
94.08082296 1
0.5%
91.78177514 1
0.5%
91.61428571 1
0.5%
90.39942837 1
0.5%
90.26773619 1
0.5%
88.17391304 1
0.5%
87.15 1
0.5%
82.2263289 1
0.5%
79.88977896 1
0.5%

Forest Area 2011
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.628654
Minimum0
Maximum98.014244
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:50.365522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45332283
Q111.024614
median30.875937
Q351.666473
95-th percentile73.284867
Maximum98.014244
Range98.014244
Interquartile range (IQR)40.641858

Descriptive statistics

Standard deviation24.420098
Coefficient of variation (CV)0.74842492
Kurtosis-0.46201148
Mean32.628654
Median Absolute Deviation (MAD)19.979273
Skewness0.53582125
Sum6819.3888
Variance596.34119
MonotonicityNot monotonic
2025-04-06T23:57:50.454504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.48445748 1
 
0.5%
8.84011952 1
 
0.5%
45.89277899 1
 
0.5%
0.940617352 1
 
0.5%
25.3595024 1
 
0.5%
34.33626392 1
 
0.5%
11.02461447 1
 
0.5%
33.15537011 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.009693053 1
0.5%
0.05730659 1
0.5%
0.062480285 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.241587575 1
0.5%
0.350729517 1
0.5%
0.351023576 1
0.5%
ValueCountFrequency (%)
98.01424359 1
0.5%
94.02143764 1
0.5%
91.73566189 1
0.5%
91.65428571 1
0.5%
90.37327617 1
0.5%
89.96976827 1
0.5%
88.36086957 1
0.5%
87 1
0.5%
81.91216777 1
0.5%
79.82156163 1
0.5%

Forest Area 2012
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.597356
Minimum0
Maximum97.952462
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:50.545104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45649497
Q110.97331
median31.04976
Q351.490159
95-th percentile73.350812
Maximum97.952462
Range97.952462
Interquartile range (IQR)40.516849

Descriptive statistics

Standard deviation24.378955
Coefficient of variation (CV)0.74788138
Kurtosis-0.45484829
Mean32.597356
Median Absolute Deviation (MAD)20.153096
Skewness0.53709907
Sum6812.8474
Variance594.33346
MonotonicityNot monotonic
2025-04-06T23:57:50.635104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.5427175 1
 
0.5%
8.753870447 1
 
0.5%
45.88730853 1
 
0.5%
0.930812347 1
 
0.5%
25.18019698 1
 
0.5%
33.86988532 1
 
0.5%
10.97330961 1
 
0.5%
33.1767257 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.009693053 1
0.5%
0.05730659 1
0.5%
0.059020543 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.241587575 1
0.5%
0.34574561 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.95246154 1
0.5%
93.96205232 1
0.5%
91.69428571 1
0.5%
91.68954865 1
0.5%
90.34712397 1
0.5%
89.67180036 1
0.5%
88.54782609 1
0.5%
86.85 1
0.5%
81.59800664 1
0.5%
79.7533443 1
0.5%

Forest Area 2013
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.555933
Minimum0
Maximum97.890679
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:50.725103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45928001
Q110.93173
median30.90768
Q351.313844
95-th percentile73.416757
Maximum97.890679
Range97.890679
Interquartile range (IQR)40.382114

Descriptive statistics

Standard deviation24.332697
Coefficient of variation (CV)0.74741207
Kurtosis-0.44566773
Mean32.555933
Median Absolute Deviation (MAD)20.025327
Skewness0.53863875
Sum6804.1899
Variance592.08015
MonotonicityNot monotonic
2025-04-06T23:57:50.815383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.60097752 1
 
0.5%
8.667621373 1
 
0.5%
45.88183807 1
 
0.5%
0.921007342 1
 
0.5%
25.00089155 1
 
0.5%
33.40350673 1
 
0.5%
10.93173048 1
 
0.5%
33.2090866 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.009693053 1
0.5%
0.055560802 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.241587575 1
0.5%
0.340467643 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.89067949 1
0.5%
93.90266701 1
0.5%
91.73428571 1
0.5%
91.6434354 1
0.5%
90.32097178 1
0.5%
89.37383244 1
0.5%
88.73478261 1
0.5%
86.7 1
0.5%
81.28384551 1
0.5%
79.68512697 1
0.5%

Forest Area 2014
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.516931
Minimum0
Maximum97.828897
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:50.904585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.46206504
Q110.903932
median30.705771
Q351.13753
95-th percentile73.482702
Maximum97.828897
Range97.828897
Interquartile range (IQR)40.233598

Descriptive statistics

Standard deviation24.296953
Coefficient of variation (CV)0.74720929
Kurtosis-0.43913141
Mean32.516931
Median Absolute Deviation (MAD)19.809107
Skewness0.54040373
Sum6796.0386
Variance590.34193
MonotonicityNot monotonic
2025-04-06T23:57:50.995586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.65923754 1
 
0.5%
8.581372299 1
 
0.5%
45.87636761 1
 
0.5%
0.911202337 1
 
0.5%
24.82158613 1
 
0.5%
32.93712814 1
 
0.5%
10.88037993 1
 
0.5%
33.23181086 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.009693053 1
0.5%
0.05210106 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.241587575 1
0.5%
0.335189677 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.82889744 1
0.5%
93.84328169 1
0.5%
91.77428571 1
0.5%
91.59732216 1
0.5%
90.29481958 1
0.5%
89.07586453 1
0.5%
88.92173913 1
0.5%
86.55 1
0.5%
80.96968439 1
0.5%
79.61690986 1
0.5%

Forest Area 2015
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.480934
Minimum0
Maximum97.767115
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:51.085658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.46485008
Q110.970175
median30.765211
Q350.935065
95-th percentile73.545735
Maximum97.767115
Range97.767115
Interquartile range (IQR)39.96489

Descriptive statistics

Standard deviation24.2628
Coefficient of variation (CV)0.74698592
Kurtosis-0.43298493
Mean32.480934
Median Absolute Deviation (MAD)19.810501
Skewness0.54180239
Sum6788.5152
Variance588.68348
MonotonicityNot monotonic
2025-04-06T23:57:51.175658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.71749756 1
 
0.5%
8.495123225 1
 
0.5%
45.87089716 1
 
0.5%
0.901397332 1
 
0.5%
24.64228071 1
 
0.5%
32.47074954 1
 
0.5%
10.83546184 1
 
0.5%
33.24564399 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.009693053 1
0.5%
0.048641318 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.241587575 1
0.5%
0.32991171 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.76711538 1
0.5%
93.78389637 1
0.5%
91.81428571 1
0.5%
91.55120891 1
0.5%
90.26866738 1
0.5%
89.10869565 1
0.5%
88.77789661 1
0.5%
86.4 1
0.5%
80.65552326 1
0.5%
79.54869249 1
0.5%

Forest Area 2016
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.453996
Minimum0
Maximum97.694359
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:51.265658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.46684509
Q111.066192
median31.101989
Q350.784902
95-th percentile73.545735
Maximum97.694359
Range97.694359
Interquartile range (IQR)39.718709

Descriptive statistics

Standard deviation24.250851
Coefficient of variation (CV)0.74723774
Kurtosis-0.43552312
Mean32.453996
Median Absolute Deviation (MAD)20.028305
Skewness0.54018367
Sum6782.8852
Variance588.10377
MonotonicityNot monotonic
2025-04-06T23:57:51.554657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.77614858 1
 
0.5%
8.408871722 1
 
0.5%
45.8654267 1
 
0.5%
0.891592327 1
 
0.5%
24.46297956 1
 
0.5%
31.63977065 1
 
0.5%
10.86308286 1
 
0.5%
33.27109973 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.00904685 1
0.5%
0.045185594 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.241587575 1
0.5%
0.324633744 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.69435897 1
0.5%
93.73716027 1
0.5%
91.85714286 1
0.5%
91.50510343 1
0.5%
90.24258664 1
0.5%
89.30434783 1
0.5%
88.4798574 1
0.5%
86.25 1
0.5%
80.34136213 1
0.5%
79.47241973 1
0.5%

Forest Area 2017
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.40457
Minimum0
Maximum97.647564
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:51.641860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.46971791
Q111.121588
median31.05211
Q350.608587
95-th percentile73.544279
Maximum97.647564
Range97.647564
Interquartile range (IQR)39.486999

Descriptive statistics

Standard deviation24.219824
Coefficient of variation (CV)0.74742
Kurtosis-0.4286352
Mean32.40457
Median Absolute Deviation (MAD)19.882955
Skewness0.54280848
Sum6772.5552
Variance586.59987
MonotonicityNot monotonic
2025-04-06T23:57:51.731848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.83479961 1
 
0.5%
8.322620219 1
 
0.5%
45.85995624 1
 
0.5%
0.881787321 1
 
0.5%
24.28368304 1
 
0.5%
30.80879176 1
 
0.5%
10.89100089 1
 
0.5%
33.29453727 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.008723748 1
0.5%
0.045185594 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.242018982 1
0.5%
0.319394586 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.6475641 1
0.5%
93.69037338 1
0.5%
91.9 1
0.5%
91.45899794 1
0.5%
90.21650589 1
0.5%
89.47826087 1
0.5%
88.18181818 1
0.5%
86.1 1
0.5%
80.027201 1
0.5%
79.39837919 1
0.5%

Forest Area 2018
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.361923
Minimum0
Maximum97.569103
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:51.822850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.47120283
Q111.19603
median31.122799
Q350.432273
95-th percentile73.544279
Maximum97.569103
Range97.569103
Interquartile range (IQR)39.236243

Descriptive statistics

Standard deviation24.195182
Coefficient of variation (CV)0.7476435
Kurtosis-0.42470609
Mean32.361923
Median Absolute Deviation (MAD)19.812266
Skewness0.54455214
Sum6763.642
Variance585.40682
MonotonicityNot monotonic
2025-04-06T23:57:51.913858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.89345064 1
 
0.5%
8.236368716 1
 
0.5%
45.85448578 1
 
0.5%
0.871982316 1
 
0.5%
24.1043842 1
 
0.5%
29.97781286 1
 
0.5%
10.91862192 1
 
0.5%
33.31727872 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.008400646 1
0.5%
0.045185594 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.243313201 1
0.5%
0.314058407 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.56910256 1
0.5%
93.64363729 1
0.5%
91.94285714 1
0.5%
91.41289246 1
0.5%
90.19042515 1
0.5%
89.67391304 1
0.5%
87.88377897 1
0.5%
85.95 1
0.5%
79.71303987 1
0.5%
79.32433865 1
0.5%

Forest Area 2019
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.308638
Minimum0
Maximum97.490577
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:52.001857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.47378143
Q111.270471
median31.125314
Q350.255959
95-th percentile73.544279
Maximum97.490577
Range97.490577
Interquartile range (IQR)38.985487

Descriptive statistics

Standard deviation24.16622
Coefficient of variation (CV)0.74798016
Kurtosis-0.41795948
Mean32.308638
Median Absolute Deviation (MAD)19.809751
Skewness0.54737343
Sum6752.5053
Variance584.00619
MonotonicityNot monotonic
2025-04-06T23:57:52.092857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
13.95210166 1
 
0.5%
8.150117213 1
 
0.5%
45.84901532 1
 
0.5%
0.862177311 1
 
0.5%
23.92508537 1
 
0.5%
29.14683397 1
 
0.5%
10.94653995 1
 
0.5%
33.33864227 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.008077544 1
0.5%
0.045185594 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.245901639 1
0.5%
0.308819249 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.49057692 1
0.5%
93.59685039 1
0.5%
91.98571429 1
0.5%
91.36678698 1
0.5%
90.16434441 1
0.5%
89.84782609 1
0.5%
87.58573975 1
0.5%
85.8 1
0.5%
79.39887874 1
0.5%
79.25029811 1
0.5%

Forest Area 2020
Real number (ℝ)

High correlation  Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.25797
Minimum0
Maximum97.412115
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2025-04-06T23:57:52.182858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.47639969
Q111.325655
median31.130231
Q350.079645
95-th percentile73.544279
Maximum97.412115
Range97.412115
Interquartile range (IQR)38.753989

Descriptive statistics

Standard deviation24.140401
Coefficient of variation (CV)0.74835462
Kurtosis-0.41214343
Mean32.25797
Median Absolute Deviation (MAD)19.785318
Skewness0.5500092
Sum6741.9158
Variance582.75897
MonotonicityNot monotonic
2025-04-06T23:57:52.271962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
14.01075269 1
 
0.5%
8.06386571 1
 
0.5%
45.84354486 1
 
0.5%
0.852372306 1
 
0.5%
23.74578653 1
 
0.5%
28.31585508 1
 
0.5%
10.97416097 1
 
0.5%
33.36000582 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
95.2%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.008077544 1
0.5%
0.045185594 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.250215703 1
0.5%
0.30348307 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.41211538 1
0.5%
93.5501143 1
0.5%
92.02857143 1
0.5%
91.32068149 1
0.5%
90.13826367 1
0.5%
90.02173913 1
0.5%
87.28770053 1
0.5%
85.65 1
0.5%
79.17625756 1
0.5%
79.08471761 1
0.5%

Country_normalized
Text

Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2025-04-06T23:57:52.431962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length22
Mean length9.3732057
Min length4

Characters and Unicode

Total characters1959
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)100.0%

Sample

1st rowaruba
2nd rowafghanistan
3rd rowangola
4th rowalbania
5th rowandorra
ValueCountFrequency (%)
and 8
 
2.7%
islands 7
 
2.3%
rep 7
 
2.3%
republic 6
 
2.0%
st 4
 
1.3%
new 3
 
1.0%
united 3
 
1.0%
arab 3
 
1.0%
guinea 3
 
1.0%
the 3
 
1.0%
Other values (244) 253
84.3%
2025-04-06T23:57:52.657864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 302
15.4%
i 177
 
9.0%
n 163
 
8.3%
e 151
 
7.7%
r 130
 
6.6%
s 103
 
5.3%
o 96
 
4.9%
91
 
4.6%
t 88
 
4.5%
l 83
 
4.2%
Other values (17) 575
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 302
15.4%
i 177
 
9.0%
n 163
 
8.3%
e 151
 
7.7%
r 130
 
6.6%
s 103
 
5.3%
o 96
 
4.9%
91
 
4.6%
t 88
 
4.5%
l 83
 
4.2%
Other values (17) 575
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 302
15.4%
i 177
 
9.0%
n 163
 
8.3%
e 151
 
7.7%
r 130
 
6.6%
s 103
 
5.3%
o 96
 
4.9%
91
 
4.6%
t 88
 
4.5%
l 83
 
4.2%
Other values (17) 575
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 302
15.4%
i 177
 
9.0%
n 163
 
8.3%
e 151
 
7.7%
r 130
 
6.6%
s 103
 
5.3%
o 96
 
4.9%
91
 
4.6%
t 88
 
4.5%
l 83
 
4.2%
Other values (17) 575
29.4%

City_normalized
Text

Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
2025-04-06T23:57:52.807583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length14
Mean length7.722488
Min length4

Characters and Unicode

Total characters1614
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)100.0%

Sample

1st roworanjestad
2nd rowkabul
3rd rowluanda
4th rowtirana
5th rowandorra la vella
ValueCountFrequency (%)
san 4
 
1.7%
city 4
 
1.7%
town 3
 
1.2%
pago 2
 
0.8%
port 2
 
0.8%
saint 2
 
0.8%
oranjestad 1
 
0.4%
johns 1
 
0.4%
andorra 1
 
0.4%
la 1
 
0.4%
Other values (220) 220
91.3%
2025-04-06T23:57:53.040463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 255
15.8%
o 125
 
7.7%
n 119
 
7.4%
i 114
 
7.1%
r 99
 
6.1%
e 96
 
5.9%
s 88
 
5.5%
t 82
 
5.1%
u 78
 
4.8%
l 73
 
4.5%
Other values (17) 485
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 255
15.8%
o 125
 
7.7%
n 119
 
7.4%
i 114
 
7.1%
r 99
 
6.1%
e 96
 
5.9%
s 88
 
5.5%
t 82
 
5.1%
u 78
 
4.8%
l 73
 
4.5%
Other values (17) 485
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 255
15.8%
o 125
 
7.7%
n 119
 
7.4%
i 114
 
7.1%
r 99
 
6.1%
e 96
 
5.9%
s 88
 
5.5%
t 82
 
5.1%
u 78
 
4.8%
l 73
 
4.5%
Other values (17) 485
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 255
15.8%
o 125
 
7.7%
n 119
 
7.4%
i 114
 
7.1%
r 99
 
6.1%
e 96
 
5.9%
s 88
 
5.5%
t 82
 
5.1%
u 78
 
4.8%
l 73
 
4.5%
Other values (17) 485
30.0%
Distinct176
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
2025-04-06T23:57:53.234655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters627
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)75.1%

Sample

1st rowora
2nd rowkab
3rd rowlua
4th rowtir
5th rowand
ValueCountFrequency (%)
san 7
 
3.3%
por 6
 
2.9%
ban 4
 
1.9%
kin 3
 
1.4%
bra 3
 
1.4%
man 3
 
1.4%
geo 2
 
1.0%
abu 2
 
1.0%
sai 2
 
1.0%
vie 2
 
1.0%
Other values (166) 175
83.7%
2025-04-06T23:57:53.487370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 98
15.6%
n 47
 
7.5%
o 43
 
6.9%
i 41
 
6.5%
r 41
 
6.5%
s 38
 
6.1%
b 36
 
5.7%
u 33
 
5.3%
m 32
 
5.1%
l 28
 
4.5%
Other values (16) 190
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 98
15.6%
n 47
 
7.5%
o 43
 
6.9%
i 41
 
6.5%
r 41
 
6.5%
s 38
 
6.1%
b 36
 
5.7%
u 33
 
5.3%
m 32
 
5.1%
l 28
 
4.5%
Other values (16) 190
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 98
15.6%
n 47
 
7.5%
o 43
 
6.9%
i 41
 
6.5%
r 41
 
6.5%
s 38
 
6.1%
b 36
 
5.7%
u 33
 
5.3%
m 32
 
5.1%
l 28
 
4.5%
Other values (16) 190
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 98
15.6%
n 47
 
7.5%
o 43
 
6.9%
i 41
 
6.5%
r 41
 
6.5%
s 38
 
6.1%
b 36
 
5.7%
u 33
 
5.3%
m 32
 
5.1%
l 28
 
4.5%
Other values (16) 190
30.3%
Distinct173
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
2025-04-06T23:57:53.680896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters627
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)69.9%

Sample

1st rowaru
2nd rowafg
3rd rowang
4th rowalb
5th rowand
ValueCountFrequency (%)
mal 5
 
2.4%
st 4
 
1.9%
uni 3
 
1.4%
tur 3
 
1.4%
bel 3
 
1.4%
gre 3
 
1.4%
gua 2
 
1.0%
mon 2
 
1.0%
slo 2
 
1.0%
bur 2
 
1.0%
Other values (163) 180
86.1%
2025-04-06T23:57:53.930906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 70
 
11.2%
r 50
 
8.0%
e 46
 
7.3%
i 42
 
6.7%
u 42
 
6.7%
n 41
 
6.5%
o 38
 
6.1%
s 38
 
6.1%
m 35
 
5.6%
l 30
 
4.8%
Other values (17) 195
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 70
 
11.2%
r 50
 
8.0%
e 46
 
7.3%
i 42
 
6.7%
u 42
 
6.7%
n 41
 
6.5%
o 38
 
6.1%
s 38
 
6.1%
m 35
 
5.6%
l 30
 
4.8%
Other values (17) 195
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 70
 
11.2%
r 50
 
8.0%
e 46
 
7.3%
i 42
 
6.7%
u 42
 
6.7%
n 41
 
6.5%
o 38
 
6.1%
s 38
 
6.1%
m 35
 
5.6%
l 30
 
4.8%
Other values (17) 195
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 70
 
11.2%
r 50
 
8.0%
e 46
 
7.3%
i 42
 
6.7%
u 42
 
6.7%
n 41
 
6.5%
o 38
 
6.1%
s 38
 
6.1%
m 35
 
5.6%
l 30
 
4.8%
Other values (17) 195
31.1%

Interactions

2025-04-06T23:57:47.378919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:35.767530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.707062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.557153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.630594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.463029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.299915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.145289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.194575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.022347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.863354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.699385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.731975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.553377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.442919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:35.835530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.771198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.618154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.694593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.527026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.365429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.210290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.259573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.090351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.927355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.764384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.795977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.616377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.498919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:35.900532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.828199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.678155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.755594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.590026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.426426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.268405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.318572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.148345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.984356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.822389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.858469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.674376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.555920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:35.966530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.888199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.730153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.812593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.649150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.485002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.324231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.375572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.208345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.045384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.883384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.913467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.732377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.614920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.029531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.954717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.786164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.868509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.713151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.550000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.384230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.437572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.266345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.104372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.941385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.971466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.791403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.670920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.101530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.022714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.847163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.927510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.769151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.607000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.443153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.494572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.327344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.167374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.007341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.029466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.846403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.730919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.168541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.083742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.902163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.986509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.828151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.669000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.507152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.552572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.385345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.231403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.065341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.084467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.904403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.790919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.235064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.143713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.140195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.046030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.889159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.729001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.564152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.608575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.445345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.293374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.124342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.144466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.961404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.848919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.298063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.203713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.197075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.105027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.945159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.788001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T23:57:45.185342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.202466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.017404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.904919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.369062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.265155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:38.314077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:39.168026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.006753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.845003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.678707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.728301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:43.562345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.410375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T23:57:47.960930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.439065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.322153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T23:57:39.227028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.063269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.903000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:41.733710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:42.788301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T23:57:46.321981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.139919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:48.019951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:36.520062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:37.378154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T23:57:39.286026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.123269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:40.968001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T23:57:43.802354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:44.640384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:45.672975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:46.496377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:47.319919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-06T23:57:53.994905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Area (km²)ContinentForest Area 2010Forest Area 2011Forest Area 2012Forest Area 2013Forest Area 2014Forest Area 2015Forest Area 2016Forest Area 2017Forest Area 2018Forest Area 2019Forest Area 2020Population Density (per km²)Population Rank
Area (km²)1.0000.101-0.095-0.095-0.097-0.100-0.102-0.104-0.106-0.108-0.109-0.109-0.111-0.589-0.825
Continent0.1011.0000.2740.2610.2600.2600.2660.2690.2680.2770.2670.2630.2630.0000.263
Forest Area 2010-0.0950.2741.0001.0001.0000.9990.9990.9980.9980.9970.9970.9960.996-0.1090.108
Forest Area 2011-0.0950.2611.0001.0001.0001.0000.9990.9990.9980.9980.9970.9970.996-0.1090.108
Forest Area 2012-0.0970.2601.0001.0001.0001.0001.0000.9990.9990.9980.9980.9980.997-0.1080.110
Forest Area 2013-0.1000.2600.9991.0001.0001.0001.0000.9990.9990.9990.9990.9980.998-0.1060.112
Forest Area 2014-0.1020.2660.9990.9991.0001.0001.0001.0001.0000.9990.9990.9990.998-0.1060.114
Forest Area 2015-0.1040.2690.9980.9990.9990.9991.0001.0001.0001.0001.0000.9990.999-0.1040.116
Forest Area 2016-0.1060.2680.9980.9980.9990.9991.0001.0001.0001.0001.0001.0000.999-0.1030.118
Forest Area 2017-0.1080.2770.9970.9980.9980.9990.9991.0001.0001.0001.0001.0001.000-0.1020.119
Forest Area 2018-0.1090.2670.9970.9970.9980.9990.9991.0001.0001.0001.0001.0001.000-0.1030.121
Forest Area 2019-0.1090.2630.9960.9970.9980.9980.9990.9991.0001.0001.0001.0001.000-0.1020.121
Forest Area 2020-0.1110.2630.9960.9960.9970.9980.9980.9990.9991.0001.0001.0001.000-0.1000.123
Population Density (per km²)-0.5890.000-0.109-0.109-0.108-0.106-0.106-0.104-0.103-0.102-0.103-0.102-0.1001.0000.119
Population Rank-0.8250.2630.1080.1080.1100.1120.1140.1160.1180.1190.1210.1210.1230.1191.000

Missing values

2025-04-06T23:57:48.244404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-06T23:57:48.376200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Country NameCapitalContinentArea (km²)Population Density (per km²)Population RankForest Area 2010Forest Area 2011Forest Area 2012Forest Area 2013Forest Area 2014Forest Area 2015Forest Area 2016Forest Area 2017Forest Area 2018Forest Area 2019Forest Area 2020Country_normalizedCity_normalizedcity_prefixcountry_prefix
0ArubaOranjestadNorth America180591.36111982.3333332.3333332.3333332.3333332.3333332.3333332.3333332.3333332.3333332.3333332.333333arubaoranjestadoraaru
1AfghanistanKabulAsia65223063.0587361.8509941.8509941.8509941.8509941.8509941.8509941.8509941.8509941.8509941.8509941.850994afghanistankabulkabafg
2AngolaLuandaAfrica124670028.54664257.87920157.43397656.98875156.54352656.09830155.65307655.20784554.76262954.31740653.87217553.426951angolaluandaluaang
3AlbaniaTiranaEurope2874898.870213828.54270128.59465328.64660628.69855828.75051128.80246428.80219028.79206228.79197128.79197128.791971albaniatiranatiralb
4AndorraAndorra la VellaEurope468170.564120334.04255334.04255334.04255334.04255334.04255334.04255334.04255334.04255334.04255334.04255334.042553andorraandorra la vellaandand
5United Arab EmiratesAbu DhabiAsia83600112.9322974.4677564.4677564.4677564.4677564.4677564.4677564.4677564.4677564.4677564.4677564.467756united arab emiratesabu dhabiabuuni
6ArgentinaBuenos AiresSouth America278040016.36833311.04034410.95871310.87708110.79545010.71381910.63218710.60039710.55983710.52037310.48017910.440715argentinabuenos airesbuearg
7ArmeniaYerevanAsia2974393.483114011.61081811.60351211.59620711.58890111.58159511.57428911.56691311.55953611.55216011.54478411.537408armeniayerevanyerarm
8American SamoaPago PagoOceania199222.477421387.15000087.00000086.85000086.70000086.55000086.40000086.25000086.10000085.95000085.80000085.650000american samoapago pagopagame
9Antigua and BarbudaSaint John’sNorth America442212.133520119.95454519.80454519.65454519.50454519.35454519.20454519.04545518.90909118.75000018.59090918.454545antigua and barbudasaint johnssaiant
Country NameCapitalContinentArea (km²)Population Density (per km²)Population RankForest Area 2010Forest Area 2011Forest Area 2012Forest Area 2013Forest Area 2014Forest Area 2015Forest Area 2016Forest Area 2017Forest Area 2018Forest Area 2019Forest Area 2020Country_normalizedCity_normalizedcity_prefixcountry_prefix
204Venezuela, RBCaracasSouth America91644530.88205153.85749153.67110753.48472353.29833953.11195552.92557152.76714552.63667652.53416552.45961152.413015venezuela rbcaracascarven
205British Virgin IslandsRoad TownNorth America151207.317922124.26666724.24000024.21333324.18666724.16000024.13333324.13333324.13333324.13333324.13333324.133333british virgin islandsroad townroabri
206Virgin Islands (U.S.)Charlotte AmalieNorth America347286.642720052.65714353.07428653.49142953.90857154.32571454.74285755.17142955.60000056.02857156.45714356.885714virgin islands uscharlotte amaliechavir
207VietnamHanoiAsia331212296.44721643.17754143.61215244.04676444.48137544.91598745.35059846.36914246.49076046.73554446.98032747.225110vietnamhanoihanvie
208VanuatuPort-VilaOceania1218926.806118136.28383936.28383936.28383936.28383936.28383936.28383936.28383936.28383936.28383936.28383936.283839vanuatuportvilaporvan
209SamoaApiaOceania284278.248418858.83038958.65936458.48833958.31731458.14629057.97526557.80565457.63604257.46643157.29682057.127208samoaapiaapisam
210Yemen, Rep.SanaaAsia52796863.8232461.0398321.0398321.0398321.0398321.0398321.0398321.0398321.0398321.0398321.0398321.039832yemen repsanaasanyem
211South AfricaPretoriaAfrica122103749.05172414.35515114.32514514.29513914.26513314.23512714.20512114.17511514.14510914.11510314.08509714.055091south africapretoriapresou
212ZambiaLusakaAfrica75261226.59766362.81494262.56180562.30866762.05552961.80239261.54925461.29595561.04288560.78970760.53651560.283337zambialusakaluszam
213ZimbabweHarareAfrica39075741.76657446.28481346.16572346.04663345.92754345.80845345.68936345.57027345.45118345.33209345.21300245.093912zimbabweharareharzim